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DCRA introduces a diffusion-conditioned representation alignment framework to enhance robustness in time-series learning, particularly for clinical signals like EEG and ECG, by aligning representations across noise levels.
This paper introduces a framework for rhetorical misalignment in language models, where presentation can induce harmful cognitive biases in human decision-making, and demonstrates this through experiments in clinical scenarios.